Abstract

Detailed physics-based computer models of fuel cells canbe computationally prohibitive for applications such as optimizationand uncertainty quantification. Such applicationscan require a very high number of runs in order to extractreliable results. Approximate models based on spatial homogeneityor data-driven techniques can serve as surrogateswhen scalar quantities such as the cell voltage are of interest.When more detailed information is required, e.g., thepotential or temperature field, computationally inexpensivesurrogate models are difficult to construct. In this paper weuse dimensionality reduction to develop a surrogate modelapproach for high-fidelity fuel cell codes in cases where thetarget is a field. A detailed 3-d model of a high-temperaturePEM fuel cell is used to test the approach. We develop aframework for using such surrogate models to quantify theuncertainty in a scalar/functional output, using the field outputresults. We propose a number of alternative methodsincluding a semi-analytical approach requiring only limitedcomputational resources.